Our goal is to analyze the latent representations learned by MCDropout models, particularly in comparison with certainty models, trained in the presence of noisy labels. Similar to the definitions presented in Bau et al [1]}, we use the term representation to describe the outputs of a particular layer in a model. More specifically: which channels of the layer have been activated for various data inputs? How strongly have these channels been activated? What is the variation in a specific channel's possible activations? Comparing the representations lends insight and intuition as to why one model may perform better than another one. Essentially, we investigate why MCDropout performs better than a certainty model by comparing the different latent representations learned by the two models respectively.
